Deep Convolution Neural Networks, or Deep CNN is the most core of the remarkable development in the field of Deep Learning. Though the CNN has been employed to solve character recognition problems in 1990s, it is not until recently that the CNN has become widespread in Machine Learning. For example, in 2012, the CNN significantly outperformed its competitors in an annual software contest, the ImageNet Large Scale Visual Recognition Challenge, and won the contest. After that, the CNN has become a very useful tool in the field of machine learning.
Meanwhile, image segmentation is a method of generating a label image using an input image, e.g., a training image or a test image. Recently, the Deep Learning has been so much widespread that the Deep Learning is also being applied to the image segmentation.
Nowadays, the Deep Learning is widely used for autonomous driving systems. For an autonomous driving system, recognizing lanes in images capable of representing the autonomous driving circumstances is very important. Thus, the autonomous driving system (i) detects all the lane candidates by using image segmentation results and (ii) classifies the lanes by using pixel embedding results, to thereby recognize the respective lanes.
However, the conventional lane recognizing technique shown above has a critical shortcoming that (i) processes for filtering non-lane road regions which were misrecognized as lanes and (ii) processes for line-fitting should be applied to outputs of the autonomous driving system as post-processing processes.